Compare · Autessa Synthetic Data vs Gretel
Autessa Synthetic Data as an alternative to Gretel
Gretel is now part of NVIDIA, and gretel.ai redirects to NVIDIA’s synthetic data page. What the Gretel team built lives on as NeMo Data Designer, an open-source Python framework, alongside NeMo Safe Synthesizer for private copies of real data. They are excellent tools for engineers building data pipelines. Autessa Synthetic Data is a finished app: describe the data in a sentence, review the rows, and fill the tables your apps and agents read.
Side by side
Autessa Synthetic Data vs Gretel
| Capability | Autessa Synthetic Data | Gretel |
|---|---|---|
| Start from one sentence, with nothing to install | ✓ | Python config: columns, samplers, models |
| AI proposes the table name and typed columns | ✓ | You define columns in code |
| Stage rows, exclude misses and regenerate a batch before anything is written | ✓ | Preview in code; review step not listed |
| Write into a live table with your own table permissions | ✓ | Dataset output; table writes not listed |
| Create a new table and fill it in one job | ✓ | Not listed |
| Near-duplicate removal and a variety ledger across the job | ✓ | Samplers for variety; dedupe not listed |
| Prompts covered by company PII and PHI stripping | ✓ | Not listed |
| Model access | Company default or your own key | Your own provider keys |
| Full headless access (Autessa MCP) | ✓ | Python SDK, REST service, coding agent skill |
| 100% customizable, code and all (Autobot) | ✓ | Open source (Apache 2.0), plugins |
| Generate with no source data | ✓ | ✓ |
| LLM training data as JSONL | ✓ | ✓ |
| Validation of generated rows | Type checks and repair | Code validators and LLM judges |
| How privacy works | Invented rows, real data never read | Differential privacy on real data |
Based on publicly available information as of November 2026. Product names are trademarks of their owners.
Where Autessa wins
Why teams pick Autessa over Gretel
- From one sentence to a schema: Autessa proposes the table name and typed columns, where Data Designer starts from a configuration you write in Python.
- A person approves the data. Review-first mode stages every row, so anyone on the team can exclude misses and regenerate a batch before a single row is written.
- Rows land in the table your app reads, written with your own permissions, or in a new table created in the same job. There is no dataset to move afterward.
- Ask from Claude. Through the Autessa MCP, Claude, Cursor or your own agent can propose a schema, run a job and collect the file, with no Python environment or provider setup.
- Open source lets engineers change the code. Autobot lets anyone do it: ask for a new preset, a review rule for your domain or a change to any screen, and your developers can still edit the code directly.
Switching takes minutes
- Take the columns from your Data Designer config or old Gretel project and describe them in a sentence.
- Accept the proposed schema and paste a few sample rows to match your style.
- Choose review-first, then fill a table or export JSONL or CSV.
FAQ
Autessa Synthetic Data vs Gretel: questions
Is Gretel still available?
Gretel is part of NVIDIA, and gretel.ai now redirects to NVIDIA’s synthetic data page. NVIDIA offers NeMo Data Designer, which the Gretel team describes as its core product, and NeMo Safe Synthesizer.
Does Autessa Synthetic Data train on my dataset?
No. It generates from your description, columns and optional sample rows. It does not train a model on your records, so differential privacy training is not needed.
Can it create LLM training data?
Yes. The training pairs preset creates system, prompt and response columns, exported as JSONL or CSV.
Data Designer is open source. How customizable is Autessa?
Every part of the app can be changed down to its code. Autobot makes the change from a plain-language request, such as a new preset or a review step, or your developers edit it directly, and there is nothing for you to host.
Can AI agents generate data through MCP?
Yes. The Autessa MCP lets Claude, ChatGPT, Cursor or your own agents propose schemas, run jobs and fetch results, within the access of whoever connected it.
Which model does it use?
Your company default model: Autessa built-in models or a model from your own provider connection.
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100% customizable, code and all
Make Autessa Synthetic Data fully your own.
Use it out of the box, then change anything. Ask Autobot, Autessa’s AI builder, for a new field, screen, report, workflow or agent and it builds it into your copy of the app. Or open the code and edit it yourself. Nothing is locked: the app, its data and its code are yours.
Start free and make it yours